# RAG Demo

Retrieval Augmented Generation using LlamaIndex with local models.

This demo builds a semantic search system over a collection of text documents
using a HuggingFace embedding model and Ollama for generation.

## Tutorial

See the full walkthrough at:
https://lem.che.udel.edu/wiki/index.php?n=Main.RAG

## Quick Start

```bash
# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Pull the generating model
ollama pull command-r7b

# Place your .txt documents in ./data, then build the vector store
python build.py

# Run interactive queries
python query.py
```

## Models

- **Embedding:** BAAI/bge-large-en-v1.5 (downloaded automatically on first run)
- **Generation:** command-r7b via Ollama